[59.05] Removing Nonlinearities from Peculiar Velocity Data

R. Watkins (Willamette U.), H.A. Feldman (U. Kansas)

Analyses of peculiar velocity surveys face several
challenges, including low signal-to-noise in individual
velocity measurements and the presence of small-scale,
nonlinear flows. We describe a new method of overcoming
these problems by using data compression as a filter with
which to separate large-scale, linear flows from small-scale
noise that can bias results. We demonstrate the
effectiveness of our method using realistic catalogs of
galaxy velocities drawn from N-body simulations.